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Federated continual learning is a decentralized approach that enables edge devices to continuously learn new data, mitigating catastrophic forgetting while collaboratively training a global model. However, existing state-of-the-art approaches in federated continual learning focus primarily on learning continuously to classify discrete sets of images, leaving dense regression tasks such as depth estimation unaddressed. Furthermore, autonomous agents that use depth estimation to explore dynamic indoor environments inevitably encounter spatial and temporal shifts in data distributions. These shifts trigger a phenomenon called spatio-temporal catastrophic forgetting, a more complex and challenging form of catastrophic forgetting. In this paper, we address the fundamental research question: “Can we mitigate spatiotemporal catastrophic forgetting in federated continual learning for depth estimation in dynamic indoor environments?”. To address this question, we propose Local Online and Continual Adaptation (LOCA), the first approach to address spatio-temporal catastrophic forgetting in dynamic indoor environments. LOCA relies on two key algorithmic innovations: online batch skipping and continual local aggregation. Our extensive experiments show that LOCA mitigates spatio-temporal catastrophic forgetting and improves global model performance, while running on-device up to 3.35× faster and consuming 3.13× less energy compared to state-of-the-art. Thus, LOCA lays the groundwork for scalable autonomous systems that adapt in real time to learn private and dynamic indoor environments.
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Morphology dictates learnability in neural controllers
Catastrophic forgetting continues to severely restrict the learnability of controllers suitable for multiple task environments. Efforts to combat catastrophic forgetting reported in the literature to date have focused on how control systems can be updated more rapidly, hastening their adjustment from good initial settings to new environments, or more circumspectly, suppressing their ability to overfit to any one environment. When using robots, the environment includes the robot's own body, its shape and material properties, and how its actuators and sensors are distributed along its mechanical structure. Here we demonstrate for the first time how one such design decision (sensor placement) can alter the landscape of the loss function itself, either expanding or shrinking the weight manifolds containing suitable controllers for each individual task, thus increasing or decreasing their probability of overlap across tasks, and thus reducing or inducing the potential for catastrophic forgetting.
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- Award ID(s):
- 1830870
- PAR ID:
- 10197971
- Date Published:
- Journal Name:
- The 2020 Conference on Artificial Life
- Page Range / eLocation ID:
- 52 to 59
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1162/isal_a_00243
